






"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import torch



class        UpperCamelCase__ (				TensorFormatter[Mapping, """torch.Tensor""", Mapping]):







    """simple docstring"""







    def __init__(		self :	List[Any]				,    UpperCamelCase_ :	Union[str, Any]=None				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           super().__init__(features=UpperCamelCase_ )
           __magic_name__      				=    torch_tensor_kwargs
           import torch  # noqa import torch at initialization







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           import torch

           if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) and column:
                  if all(
                      isinstance(UpperCamelCase_				,    torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                      for x in column ):
                         return torch.stack(UpperCamelCase_ )
           return column







    def 				a__			(		self :	str				,    UpperCamelCase_ :	str ):





           '''simple docstring'''

           import torch

           if isinstance(UpperCamelCase_				,    (str, bytes, type(UpperCamelCase_ )) ):
                  return value
           elif isinstance(UpperCamelCase_				,    (np.character, np.ndarray) ) and np.issubdtype(value.dtype				,    np.character ):
                  return value.tolist()

           __magic_name__      				=    {}

           if isinstance(UpperCamelCase_				,    (np.number, np.ndarray) ) and np.issubdtype(value.dtype				,    np.integer ):
                  __magic_name__      				=    {'dtype': torch.intaa}
           elif isinstance(UpperCamelCase_				,    (np.number, np.ndarray) ) and np.issubdtype(value.dtype				,    np.floating ):
                  __magic_name__      				=    {'dtype': torch.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                  import PIL.Image

                  if isinstance(UpperCamelCase_				,    PIL.Image.Image ):
                         __magic_name__      				=    np.asarray(UpperCamelCase_ )
           return torch.tensor(UpperCamelCase_				,    **{**default_dtype, **self.torch_tensor_kwargs} )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Any ):





           '''simple docstring'''

           import torch

           # support for torch, tf, jax etc.
           if hasattr(UpperCamelCase_				,    '__array__' ) and not isinstance(UpperCamelCase_				,    torch.Tensor ):
                  __magic_name__      				=    data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(UpperCamelCase_				,    np.ndarray ):
                  if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                         return self._consolidate([self.recursive_tensorize(UpperCamelCase_ ) for substruct in data_struct] )
           elif isinstance(UpperCamelCase_				,    (list, tuple) ):
                  return self._consolidate([self.recursive_tensorize(UpperCamelCase_ ) for substruct in data_struct] )
           return self._tensorize(UpperCamelCase_ )







    def 				a__			(		self :	int				,    UpperCamelCase_ :	dict ):





           '''simple docstring'''

           return map_nested(self._recursive_tensorize				,    UpperCamelCase_				,    map_list=UpperCamelCase_ )







    def 				a__			(		self :	Tuple				,    UpperCamelCase_ :	pa.Table ):





           '''simple docstring'''

           __magic_name__      				=    self.numpy_arrow_extractor().extract_row(UpperCamelCase_ )
           __magic_name__      				=    self.python_features_decoder.decode_row(UpperCamelCase_ )
           return self.recursive_tensorize(UpperCamelCase_ )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	pa.Table ):





           '''simple docstring'''

           __magic_name__      				=    self.numpy_arrow_extractor().extract_column(UpperCamelCase_ )
           __magic_name__      				=    self.python_features_decoder.decode_column(UpperCamelCase_				,    pa_table.column_names[0] )
           __magic_name__      				=    self.recursive_tensorize(UpperCamelCase_ )
           __magic_name__      				=    self._consolidate(UpperCamelCase_ )
           return column







    def 				a__			(		self :	str				,    UpperCamelCase_ :	pa.Table ):





           '''simple docstring'''

           __magic_name__      				=    self.numpy_arrow_extractor().extract_batch(UpperCamelCase_ )
           __magic_name__      				=    self.python_features_decoder.decode_batch(UpperCamelCase_ )
           __magic_name__      				=    self.recursive_tensorize(UpperCamelCase_ )
           for column_name in batch:
                  __magic_name__      				=    self._consolidate(batch[column_name] )
           return batch







"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 							A						(				__snake_case: Optional[int]  )    ->		Tuple:

       """simple docstring"""





       for param in module.parameters():
              __magic_name__      				=    False


def 							A						(				)    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              __magic_name__      				=    'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'  )
       return device


def 							A						(				__snake_case: int  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    plt.imshow(__snake_case  )
       fig.axes.get_xaxis().set_visible(__snake_case  )
       fig.axes.get_yaxis().set_visible(__snake_case  )
       plt.show()






def 							A						(				)    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    datetime.now()
       __magic_name__      				=    current_time.strftime('%H:%M:%S'  )
       return timestamp